In [50]:
#loading needed libraries
library(tidyverse)
library(repr)
library(infer)
library(cowplot)
library(broom)
library(GGally)
library(modelr)
library(dplyr)
In [76]:
options(repr.plot.width = 15, repr.plot.height =5)
In [77]:
repositories <- read_csv("repositories.csv") # reading data 

repositories <- repositories %>%
  rename(Has_discussions = `Has Discussions`,
         Has_projects = `Has Projects`,
         Has_wiki = `Has Wiki`,
         Has_pages = `Has Pages`,
         is_template = `Is Template`) %>% 
  mutate(Has_discussions = as.factor(Has_discussions),
        Has_projects = as.factor(Has_projects),
         Has_wiki = as.factor(Has_wiki),
         Has_pages = as.factor(Has_pages)) %>%
  select(Stars, Size, Forks, Has_discussions, Has_projects, Has_wiki, Has_pages, is_template, Issues) |> # selecting relevant columns
  drop_na()

head(repositories)

Rows: 113821 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): Name, Description, URL, Homepage, Language, License, Topics, Defau...
dbl  (5): Size, Stars, Forks, Issues, Watchers
lgl  (9): Has Issues, Has Projects, Has Downloads, Has Wiki, Has Pages, Has ...
dttm (2): Created At, Updated At

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
A tibble: 6 × 9
StarsSizeForksHas_discussionsHas_projectsHas_wikiHas_pagesis_templateIssues
<dbl><dbl><dbl><fct><fct><fct><fct><lgl><dbl>
37407438745133599FALSETRUE FALSETRUE FALSE 248
298393 1708757194FALSEFALSEFALSETRUE FALSE 46
269997 144126485FALSEFALSEFALSETRUE FALSE 61
26790118779921497FALSEFALSEFALSEFALSEFALSE16712
265161 2099869434FALSEFALSEFALSEFALSEFALSE 56
256615 508829254FALSEFALSEFALSEFALSEFALSE 191
In [78]:
repositories_sample <- repositories %>%
  sample_n(2000)
In [84]:
# table for summary statisitcs of continuous variables
summary_table <- data.frame(
  Variable = c("Size", "Stars", "Issues"),
  Min = c(0, 353, 0),
  `1st Qu.` = c(562, 486, 5),
  Median = c(3402, 751, 16),
  Mean = c(63441, 1893, 56.78),
  `3rd Qu.` = c(20286, 1546, 45),
  Max = c(104880243, 374074, 26543))

summary_table
A data.frame: 3 × 7
VariableMinX1st.Qu.MedianMeanX3rd.Qu.Max
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
Size 0562340263441.0020286104880243
Stars 353486 751 1893.00 1546 374074
Issues 0 5 16 56.78 45 26543

All the selected continuous variables in our dataset had right-skewed distributions. This skewness, as well as the extremly large ranges and the presence of extreme outliers, could potentially distort statistical analyses and reduce the interpretability of our data. This indicated the need for a transformation to address these challenges. Applying a log transformation seemed appropriate as it helps stabilize the variance, making the data more homoscedastic, and lessens the impact of extreme values. This approach makes the modeling process more reliable and helps us better understand the relationships between variables, especially when making predictions.

In [79]:
p1 <- ggplot(repositories, aes(x = log(Size))) +
  geom_histogram(bins = 30, fill = "steelblue", alpha = 0.7) +
  ggtitle("Histogram of log(Size)") +
  xlab("log(Size)") +
  ylab("Frequency") +
  theme_minimal()

p1

Warning message:
“Removed 78 rows containing non-finite outside the scale range (`stat_bin()`).”
No description has been provided for this image
In [55]:
#plot 2: scatterplot of stars vs forks, point size represents repository size and is colored for has_discussions
p2 <- repositories_sample %>%
    ggplot(aes(x = Forks, y = Stars, color = Has_discussions, size = Size)) +  
    geom_point(alpha = 0.6) +  # semi transparent points to make it easier to visualize even when overlapping
    scale_size(range = c(1, 10)) +  # adjusting size of the points based on size of repository
    scale_color_manual(values = c("red", "blue")) + 
    labs(title = "Combo Scatterplot: Forks, Stars, and Repository Size (Colored by Has Discussions)",
         x = "(Logged) Forks",
         y = "(Logged) Stars",
         size = "Repository Size",
         color = "Has Discussions") +
    scale_x_log10() +
    scale_y_log10() +
    theme_minimal()

p2
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For this combination scatterplot, there seems to be a positive association between (logged) Forks and (logged) Stars, with repositories having discussions enabled (blue) being more common among those with higher star and fork counts. When examining the sizes of the data points, there doesn’t appear to be a clear pattern based on Star levels, suggesting a very weak or nonexistent relationship between (logged) Stars and repository Size.

In [65]:
repositories <- repositories %>%
  mutate(across(c(Has_projects, Has_wiki, is_template), as.factor))

p3 <- repositories %>%
  pivot_longer(cols = c(Has_projects, Has_wiki, is_template),
               names_to = "Variable",
               values_to = "Value") %>%
  ggplot(aes(x = Value, y = log(Stars), fill = Variable)) +
  geom_boxplot(alpha = 0.7) +
  facet_wrap(~ Variable, ncol = 4) +
  labs(title = "Boxplots of Stars by Repository Features",
       x = "Feature Value",
       y = "Log(Stars)") +
  theme_minimal() +
  theme(legend.position = "none")

p3
No description has been provided for this image

For the Has_projects boxplot, it appears that repositories without projects tend to have a higher average number of stars. Similarly, the Has_wiki boxplot suggests that repositories without an associated wiki tend to have a higher average number of stars. Lastly, for the is_template boxplot, the number of stars does not vary significantly based on whether a repository is a template, although the average is slightly higher for repositories that are not templates. Overall, none of these differences seem substantial at first glance, which might indicate a lack of a strong relationship between the number of stars and these variables.